Emerging Technology: New Opportunities for the Community Sector. Editorial
Bibliographic record
Abstract
This issue of the Journal of Community, Citizen's and Third Sector Media and Communication brings together research papers that seek to continue a dialogue about key questions started in the last issue of 3CMedia. One of these key questions deals with the continuing quest to find the raison d’etre for community organisations (including community media organisations) in times of participatory culture, media convergence and Web 2.0. Do the affordances of these socio-cultural and technical trends render the third sector less significant or even obsolete as some commentators speculate? Citizen journalism challenges conventional notions of news reporting. Users of blogs and social networking sites display and discuss their political, civic and environmental concerns on their profiles through personal statements, online group affiliations and virtual badges. Taking advantage of peer to peer forms of electronic communication such as mobile phone text messaging, the Critical Mass movement has established a history of successfully organising large political demonstrations in a decentralised manner without the need for a single dedicated institutional entity to coordinate the efforts. Indeed, in Shirky’s words, 'Here Comes Everybody' (2008).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".